The subject matter disclosed herein relates to performance characteristic calculation and comparison.
Current processes for calculating performance degradation of, for example, gas turbine engines that are provided as part of a fleet of gas turbine engines use site-specific correction factors and require a substantial amount of manual effort. Generally, the processes for a particular site involve conducting a performance test at a specified interval, usually every one year, with the test engineer conducting the test at baseload operation. Performance degradation data is gathered from the test results and correction factors for the site are applied to obtain the corrected output and heat rate of the site. Percentage output and heat rate degradation values for the site are then calculated using the corrected output and heat rate values and the site-specific reference values. Degradation percentage values are then compared to contractually guaranteed outputs and heat rate degradation values to identify whether the site is performing better or worse than the guarantee. Bonus or liquidated damage (LD) amounts are then calculated using variations from the guarantee and bonus/LD rates. These processes are repeated for all required sites.
In addition, if the degradation is higher than the guarantee, a root cause analysis is performed to identify areas of improvement and recommendations may be provided to improve the performance of the assets at each site. If the improvement measures are implemented, the benefits of those measures are not realized until the next performance test.
Thus, potential problems with current processes are that information related to degradation is available only after a performance test with little to no information being available to the test engineer on the amount of degradation prior to the test. Thus, there is little to no room or opportunity for improvement during off-test periods with little to no line-of-sight between subsequent performance tests. Also, cycle time to identify and mitigate risk is high, degradation information on a whole fleet is difficult to view, identifying the most degraded machine or the machine that needs the most immediate attention is difficult and effects of intermittent maintenance actions such as water washes, leak fixes, filter cleaning, etc., cannot be identified or tracked. Finally, there is often no central database available to store test results.